INFORMATION-BASED SEARCH

A successful algorithm for autonomous search needs to be able to use available information to predict what actions are likely to result in useful measurements, and to choose between those actions based on energy or time constraints. This work focuses on developing mathematical theory for balancing these factors and using that theory to formulate an algorithm for robotic search, called “Ergodic Exploration for Distributed Information” (EEDI). The closed-loop EEDI algorithm involves several components, graphically illustrated below.

The necessary components for a general closed-loop, information-based sensing algorithm.

What differentiates the EEDI algorithm from other approaches to similar problems is the use of ergodic optimal control. The measurement model and belief on the estimates, which will both vary based on a particular sensor and search objective, are used to create a spatial map of expected information gain using Fisher information. Fisher information provides a way of translating knowledge about the physics of a particular sensor and the history of past measurements into a map representing where the robot should move to obtain the most useful measurements in the future. This map can be used to plan control actions using ergodic optimal control. In experimental and simulated work, ergodic trajectory optimization with respect to the expected information distribution (part C, above) is shown to outperform alternative information maximization, entropy minimization, and random walk controllers in scenarios when the signal to noise ratio is low or in the presence of disturbances.

the sensorpod: motivating example and Experimental platform

The SensorPod robot, developed by Malcolm MacIver’s research group, is inspired by a type of tropical fish. The SensorPod is an example of a system requiring careful consideration of the sensor characteristics while planning. Both the fish and the SensorPod use a sensing modality called electrolocation to acquire information about their environment. Electrolocation involves measuring disturbances in a self-generated weak electric field, and enables the fish and the robot to sense in low-light and in cluttered environments. For more information on the SensorPod robot, electrolocation as a sensing modality, and a graphical overview of the closed-loop algorithm, see the video below. The video is narrated by Prof. MacIver.

Additional example: target localization using range measurements

In the example shown below below, the EEDI algorithm is used to localize a round target in a planar environment, in the presence of several distractor objects that are similar, but not identical to, the target object. In this case, the belief space (target location) is the planar environment depected in the left column. The evolution of the belief (heatmap) is plotted, as a function of EEDI algorithm. The sensor configuration (and corresponding control variables are the one-dimensional position of the sensor and the bearing angle. On the left, the position of the sensor is indicated with filled black circles, with simulated range measurements plotted in green at discrete intervals. The expected information, plotted over the sensor configuration space, and corresponding optimally ergodic trajectories, are plotted on the right. Similar to experimental observations made using the SensorPod system, EEDI is more robust in situations where information maximization strategies (e.g. high variance or noise, multimodal distributions) are likely to fail.